A Global Model-Agnostic Rule-Based XAI Method based on Parameterised Event Primitives for Time Series Classifiers

Introducing a new global model agnostic rule-based XAI method tailored for deep learning based time series classifiers. While there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data, adapting these methods for explaining deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. The temporal nature of time series data adds complexity, necessitating a specialized approach.

Our project addresses this challenge with a novel methodology tailored for deep learning time series classifiers. The primary objective of this project is to offer a clear and interpretable understanding of deep learning-based time series classifiers while maintaining the temporal relationship in the sequence. The proposed solution aims to generate decision trees as explanations, providing insights that are comprehensible for human interpretation.

File description

• experiments: This directory contains code files and results.

  • results: Includes experiment results for each dataset.

    • output.log: This file contains the objective evaluation of the method for each dataset and model type.
    • Example: Evaluation result for FCN model architecture trained on the ECG dataset can be found at experiments\results\simulation\ecg200\fcn--2024-01-08_16-01-07\output.log.

Method Design

Objective Evaluation Metrics

Table 1. Objective Evaluation Metrics for Rule-Based Explanation

Metric	Definition	Formula
Accuracy	Proportion of correctly predicted instances (c) out of the total instances (N)	A = c / N
Fidelity	Ratio of input instances where the surrogate model agrees (a) with the actual model, divided by the total number of instances (N)	F = a / N
Complexity	The complexity or simplicity of the generated explanation is measured by the number of nodes and depth	C = #Depth, #Nodes
Robustness	The persistence of methods to withstand small perturbations (δ) of the input that does not change the prediction of the model	R = Σ[g(x_n)=g(x_n+δ)] / N

Result

Table 4. Mean and standard deviation of the objective evaluation of the rule-based explanation for LSTM-FCN model

Dataset	Acc	Fidelity	#Depth	#Node	Rob.
ECG200	0.80±0.12	0.89±0.06	4±2	10±5	0.76±0.14
GunPoint	0.73±0.11	0.88±0.07	4±2	12±5	0.64±0.17
FordA	0.79±0.04	0.84±0.05	8±4	41±38	0.77±0.05
FordB	0.81±0.04	0.86±0.05	7±4	37±33	0.79±0.08

Table 4. Mean and standard deviation of the objective evaluation of the rule-based explanation for FCN model

Dataset	Acc	Fidelity	#Depth	#Node	Rob.
ECG200	0.79±0.10	0.89±0.06	3±2	10±6	0.78±0.12
GunPoint	0.74±0.12	0.88±0.11	4±2	12±5	0.64±0.18
FordA	0.78±0.03	0.84±0.04	8±3	42±34	0.76±0.04
FordB	0.81±0.04	0.87±0.05	8±4	42±34	0.77±0.06

Usage

To run the the simulation of the experiment, use the following command:

• For FCN model

python fcn_simulation --dataset [dataset-name] --num_runs [100 ]  --class_labels [list of the class names]

• For LSTM-FCN model

python lstm_fcn_simulation --dataset [dataset-name] --num_runs [100 ] --class_labels [list of the class names]

Requirments